In American Sign Language (ASL) the structure of signed sentences is conveyed by grammatical markers which are represented by facial feature movements and head motions. Without recovering grammatical markers, a sign language recognition system cannot fully reconstruct a signed sentence. However, this problem has been largely neglected in the literature. In this paper, we propose to use a 2-layer Conditional Random Field model for recognizing continuously signed grammatical markers in ASL. This recognition requires identifying both facial feature movements and head motions while dealing with uncertainty introduced by movement epenthesis and other effects. We used videos of the signers’ faces, recorded while they signed simple sentences containing multiple grammatical markers. In our experiments, the proposed classifier yielded a precision rate of 93.76% and a recall rate of 85.54%.
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